In a matter of years, data has transformed
from an intermittent input to a real-time resource — and utilities are still
nowhere close to taking full advantage of it.

The ever-widening torrent of data, made possible by the Internet of Things, machine learning and smart meters, has created a gap. This gap lies between the volume of information available to decision makers, and the systems they have in place to analyse and put it to good use.

Think of it this way:

As competition in the industry increases, so
does the importance of narrowing the gap — that is, increasing the overlap of
data volume and processing abilities — for all utilities who want to stay
competitive.

So, what’s the strategy here?

The first step is to understand that a key
element of the gap is an incompatibility between old models and new data
sources.

The logic and processing used in traditional
decision-making systems are based on fixed rules. Meanwhile, modern
developments like machine learning and advanced metering technology have turned
data into smart data:
dynamically-sourced information in a self-improving network.

The takeaway? To fully capitalise on smart
data, utilities have to employ smart
models to process it.

That means phasing out one-size-fits-all rules
and opaque algorithms in favor of performance-monitored intelligent models that
can adapt to changing conditions.

The best part about models with performance
and accuracy-monitoring built in is that they grant real confidence in
decision-making.

No more relying on thousands of algorithms to drive alerts and choices without any way of knowing their individual effectiveness; if an algorithm isn’t achieving a desired outcome, the performance report will show it. You’ll then have the option to tweak the formula or get rid of it altogether.

Acquiring your toolbox

For many in the industry, the idea of having a
suite of invaluable tools like this at their disposable still seems like a
fantasy. A common assumption is that integration costs would be too high, and
current systems too entrenched, for an average utility to realistically throw
out its old decision-making models and replace them with new ones.

There’s some truth in that. Organisational
change is difficult and expensive, especially when it involves adjustments as
comprehensive as these.

While committing to an internal system
overhaul has its benefits, there’s a case to be made about a superior way to
achieve the same results: partnering with an analytical services organisation.

Services like SAS Intelligent Decisioning from analytics software provider SAS, offer utilities the tools to rapidly turn their reams of asset data into high-quality decisions — without the arduous, trial-and-error process of revamping the back office.

“In addition to the cost and time benefits, partnering with an analytics leader offers a significant advantage over in-house analytics in that you don’t have to worry about constantly upgrading your systems,” said Grant Dyer, Energy, Utilities & Telecommunications Industry Lead for SAS Australia & New Zealand.

“Instead, the provision of strong governance and model management lineage helps to stay on top of new technology and trends, supporting connected analytics (via API) which is crucial to decisioning.

“You can rely on a quality service provider like SAS to have that covered from inception to production and operations.”

To get more specific, an effective decision management service should empower utilities to:

Detect potential failures of
assets before they happen, saving money and resources